{"title":"利用基于人工智能的图像分析检测口腔癌和口腔潜在恶性疾病。","authors":"Atsumu Kouketsu DDS, PhD, Chiaki Doi PhD, Hiroaki Tanaka BS, Takashi Araki BS, Rina Nakayama BS, Tsuguyoshi Toyooka PhD, Satoshi Hiyama PhD, Masahiro Iikubo DDS, PhD, Ken Osaka MD, PhD, Keiichi Sasaki DDS, PhD, Hirokazu Nagai DDS, PhD, Tsuyoshi Sugiura DDS, PhD, Kensuke Yamauchi DDS, PhD, Kanako Kuroda DDS, PhD, Yuta Yanagisawa DDS, PhD, Hitoshi Miyashita DDS, PhD, Tomonari Kajita DDS, PhD, Ryosuke Iwama DDS, PhD, Tsuyoshi Kurobane DDS, PhD, Tetsu Takahashi DDS, PhD","doi":"10.1002/hed.27843","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera.</p>\n </section>\n \n <section>\n \n <h3> Subjects and methods</h3>\n \n <p>We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (<i>n</i> = 66), leukoplakia (<i>n</i> = 49), and other oral diseases (<i>n</i> = 405).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Our proposed model is a potential diagnostic tool for oral diseases.</p>\n </section>\n </div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hed.27843","citationCount":"0","resultStr":"{\"title\":\"Detection of oral cancer and oral potentially malignant disorders using artificial intelligence-based image analysis\",\"authors\":\"Atsumu Kouketsu DDS, PhD, Chiaki Doi PhD, Hiroaki Tanaka BS, Takashi Araki BS, Rina Nakayama BS, Tsuguyoshi Toyooka PhD, Satoshi Hiyama PhD, Masahiro Iikubo DDS, PhD, Ken Osaka MD, PhD, Keiichi Sasaki DDS, PhD, Hirokazu Nagai DDS, PhD, Tsuyoshi Sugiura DDS, PhD, Kensuke Yamauchi DDS, PhD, Kanako Kuroda DDS, PhD, Yuta Yanagisawa DDS, PhD, Hitoshi Miyashita DDS, PhD, Tomonari Kajita DDS, PhD, Ryosuke Iwama DDS, PhD, Tsuyoshi Kurobane DDS, PhD, Tetsu Takahashi DDS, PhD\",\"doi\":\"10.1002/hed.27843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Subjects and methods</h3>\\n \\n <p>We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (<i>n</i> = 66), leukoplakia (<i>n</i> = 49), and other oral diseases (<i>n</i> = 405).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Our proposed model is a potential diagnostic tool for oral diseases.</p>\\n </section>\\n </div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hed.27843\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/hed.27843\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hed.27843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Detection of oral cancer and oral potentially malignant disorders using artificial intelligence-based image analysis
Background
We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera.
Subjects and methods
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
Results
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
Conclusions
Our proposed model is a potential diagnostic tool for oral diseases.
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